An embodiment relates to augmented curb and bump detection.
Advanced Driving Assistance Systems (ADAS) is often viewed as an intermediate stage before reaching full autonomous driving. ADAS functionality integrates various active safety features. A goal is to alert the driver of possible danger and prevent a collision whether it is a pedestrian, another vehicle, or object (e.g., pedestrian/vehicle detection, lane departure warming, and lane keeping assist). Many advanced systems of the vehicle (e.g., automatic cruise control, lane change demand, parking assist) take partial control of the vehicle such as autonomously modifying the speed and/or steering while taking into account the surrounding environment.
Curb detection contributes to accurate vehicle positioning in urban areas. The detection of curbs in front/rear of the vehicle is crucial for applications such as parking assist/autonomous parking. The accurate localization and range estimation of the curb is passed to the vehicle control system, which in turn smoothly maneuvers the vehicle so as to avoid possible shock with the front/rear curb or to avoid curbs around a curvature in the road.
The challenge in extracting curbs and bump from images lies in their small size (e.g., approximately 10-20 cm high). While the curbs three dimensional (3D) shape is pretty standard, it would be possible to model the curb as a two dimensional (2D) step-function. Consequently, most current approaches rely on active sensor (Lidar) or stereo-camera, which make it possible to directly extract 3D information which assists in simplifying the processing. Such techniques often use various road marks (e.g., soft shoulder, curbs, and guardrails, based on strong prior knowledge about road scenes).
While some systems rely on various techniques such as deep learning, hierarchical probabilistic graphical model, integrate prior knowledge, or exploit multi-sensor fusion, none of these general frameworks deal explicitly with curb detection because of their small size and its non-distinctive pattern require a dedicated approach to be successful.